semi-intact forest = primary forest
secondary forest
brushy regrowth = savoka
agriculture = sugar cane, mixed agriculture, coffee, banana
agroforest = vanilla
flooded rice = rice
village
Merging similar land uses together:
1. semi-intact forest
2. agriculture = secondary_forest + brushy_regrowth + agriculture +
agroforest
3. flooded_rice
4. village
Exploration of small mammals abundance and richness across land
use
| village | host_species | n |
|---|---|---|
| Andatsakala | Mus musculus | 83 |
| Andatsakala | Rattus rattus | 235 |
| Mandena | Microgale brevicaudata | 184 |
| Mandena | Mus musculus | 86 |
| Mandena | Rattus rattus | 345 |
| Sarahandrano | Mus musculus | 81 |
| Sarahandrano | Rattus rattus | 297 |
Filtering ASVs by relative abundance
ASVs with relative reads count of less than x% within a sample are
filtered out
Number of ASVs
| t | Microgale brevicaudata | Mus musculus | Rattus rattus |
|---|---|---|---|
| 0.0 | 10655 | 5124 | 10353 |
| 0.1 | 3901 | 2888 | 7058 |
| 0.2 | 2172 | 2078 | 5316 |
| 0.3 | 1509 | 1648 | 4276 |
| 0.4 | 1136 | 1406 | 3593 |
| 0.5 | 937 | 1232 | 3085 |
| 1.0 | 518 | 739 | 1809 |
| 2.0 | 307 | 409 | 998 |
Number of ASVs
| t | grid | Microgale brevicaudata | Mus musculus | Rattus rattus |
|---|---|---|---|---|
| 0.0 | semi-intact_forest | 2366 | NA | 1748 |
| 0.0 | agriculture | 7164 | 4367 | 8959 |
| 0.0 | flooded_rice | 3740 | 1800 | 3844 |
| 0.0 | village | NA | 1252 | 4897 |
| 0.1 | semi-intact_forest | 721 | NA | 1139 |
| 0.1 | agriculture | 2542 | 2467 | 6136 |
| 0.1 | flooded_rice | 1700 | 1210 | 2535 |
| 0.1 | village | NA | 825 | 3308 |
| 0.2 | semi-intact_forest | 405 | NA | 750 |
| 0.2 | agriculture | 1437 | 1731 | 4609 |
| 0.2 | flooded_rice | 958 | 959 | 1841 |
| 0.2 | village | NA | 642 | 2401 |
| 0.5 | semi-intact_forest | 196 | NA | 344 |
| 0.5 | agriculture | 641 | 1017 | 2669 |
| 0.5 | flooded_rice | 409 | 594 | 918 |
| 0.5 | village | NA | 365 | 1341 |
| 1.0 | semi-intact_forest | 124 | NA | 168 |
| 1.0 | agriculture | 377 | 595 | 1525 |
| 1.0 | flooded_rice | 214 | 373 | 484 |
| 1.0 | village | NA | 200 | 763 |
All ASVs with less than 0.01 relative abundance within a
sample were filtered out
Total number of reads
[1]
“mean: 28518.4992343032” [1] “median: 25516.5” [1] “range: 7” “range:
166634”
How many individuals have less than X reads
| reads_threshold | n |
|---|---|
| 0 | 1306 |
| 1000 | 1290 |
| 5000 | 1274 |
| 10000 | 1245 |
| 15000 | 1180 |
| 20000 | 982 |
| 25000 | 672 |
| 30000 | 431 |
| 35000 | 270 |
| 40000 | 187 |
| 45000 | 134 |
| 50000 | 104 |
How many individuals have less than X reads
| reads_threshold | Microgale brevicaudata | Mus musculus | Rattus rattus |
|---|---|---|---|
| 0 | 182 | 250 | 874 |
| 1000 | 173 | 248 | 869 |
| 5000 | 173 | 242 | 859 |
| 10000 | 172 | 232 | 841 |
| 15000 | 164 | 213 | 803 |
| 20000 | 130 | 177 | 675 |
| 25000 | 73 | 126 | 473 |
| 30000 | 33 | 92 | 306 |
| 35000 | 22 | 62 | 186 |
| 40000 | 16 | 55 | 116 |
| 45000 | 12 | 43 | 79 |
| 50000 | 7 | 41 | 56 |
How many individuals have less than X reads
| reads_threshold | Andatsakala | Mandena | Sarahandrano |
|---|---|---|---|
| 0 | 317 | 612 | 377 |
| 1000 | 315 | 599 | 376 |
| 5000 | 313 | 592 | 369 |
| 10000 | 305 | 580 | 360 |
| 15000 | 277 | 554 | 349 |
| 20000 | 220 | 431 | 331 |
| 25000 | 149 | 236 | 287 |
| 30000 | 96 | 110 | 225 |
| 35000 | 66 | 67 | 137 |
| 40000 | 53 | 48 | 86 |
| 45000 | 43 | 33 | 58 |
| 50000 | 38 | 23 | 43 |
All host individuals with less than 1000 total reads were
filtered out
In how many villages ASVs occur
In how many seasons ASVs occur
In how many grids ASVs occur
In how many grids ASVs occur
In how many individuals ASVs occur
Given by the proportion of individuals out of the total abundance of the
focal host species
In how many individuals ASVs occur
In how many individuals ASVs occur
Given by the proportion of individuals out of the total abundance of the
focal host species in the grid
In how many host species ASVs occur - shared ASVs
Calculating average (per host) alpha diversity measures
No normalization applied
Average observed ASVs richness
Average Shannon diversity
Average phylogenetic species evenness
Independent variable: ASVs diversity (observed richness,
shannon)
Dependent variables: village, grid, season, sex, mass
The best models: delta AICc <= 2
ASVs richness -
| (Intercept) | grid | mass | season | sex | village | df | logLik | AICc | delta | weight |
|---|---|---|---|---|---|---|---|---|---|---|
| 17.81208 | + | NA | NA | NA | NA | 4 | -812.2850 | 1632.735 | 0.0000000 | 0.140096881 |
| 20.37933 | + | -0.2178237 | NA | NA | NA | 5 | -811.4301 | 1633.108 | 0.3734279 | 0.116235728 |
| 17.41129 | NA | NA | NA | NA | NA | 2 | -815.0346 | 1634.118 | 1.3835373 | 0.070145071 |
| 18.62282 | + | NA | NA | NA | + | 6 | -811.1458 | 1634.640 | 1.9055789 | 0.054030285 |
| 21.53217 | + | -0.2416901 | NA | NA | + | 7 | -810.0989 | 1634.665 | 1.9299353 | 0.053376285 |
| 21.80403 | + | -0.2296774 | + | + | + | 10 | -809.4687 | 1639.866 | 7.1310964 | 0.003962147 |
| rowname | Estimate | Std. Error | t value | p |
|---|---|---|---|---|
| (Intercept) | 21.804034 | 2.367732 | 9.208827 | 0.0000000 |
| gridvillage | -2.796999 | 1.354910 | -2.064342 | 0.0400646 |
ASVs shannon -
| (Intercept) | grid | mass | season | sex | village | df | logLik | AICc | delta | weight |
|---|---|---|---|---|---|---|---|---|---|---|
| 1.467261 | + | NA | NA | NA | + | 6 | -136.3021 | 284.9528 | 0.000000 | 0.313990806 |
| 1.437283 | + | NA | NA | + | + | 7 | -135.9680 | 286.4027 | 1.449913 | 0.152080070 |
| 1.560507 | NA | NA | NA | NA | NA | 2 | -143.5431 | 291.1351 | 6.182321 | 0.014270628 |
| 1.501380 | + | -0.0038554 | + | + | + | 10 | -135.5505 | 292.0293 | 7.076557 | 0.009125613 |
| rowname | Estimate | Std. Error | t value | p |
|---|---|---|---|---|
| (Intercept) | 1.5013797 | 0.1563779 | 9.600969 | 0.0000000 |
| villageMandena | 0.1500372 | 0.0665795 | 2.253505 | 0.0251344 |
| gridvillage | 0.2871536 | 0.0894857 | 3.208934 | 0.0015147 |
ASVs Phylo -
| (Intercept) | grid | mass | season | sex | village | df | logLik | AICc | delta | weight |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.1962168 | + | NA | NA | NA | NA | 4 | 260.8415 | -513.5184 | 0.0000000 | 0.16061177 |
| 0.1659562 | + | 0.0025675 | NA | NA | NA | 5 | 261.5222 | -512.7964 | 0.7219601 | 0.11194526 |
| 0.2042178 | NA | NA | NA | NA | NA | 2 | 258.1454 | -512.2418 | 1.2765597 | 0.08483518 |
| 0.1968718 | + | NA | + | NA | NA | 6 | 261.9655 | -511.5825 | 1.9358661 | 0.06101118 |
| 0.1616796 | + | 0.0021037 | + | + | + | 10 | 263.4562 | -505.9841 | 7.5342620 | 0.00371307 |
| rowname | Estimate | Std. Error | t value | p |
|---|---|---|---|---|
| (Intercept) | 0.1616796 | 0.0312925 | 5.166726 | 0.0000005 |
| gridvillage | 0.0404207 | 0.0179068 | 2.257279 | 0.0248941 |
ASVs richness -
| (Intercept) | grid | mass | season | sex | village | df | logLik | AICc | delta | weight |
|---|---|---|---|---|---|---|---|---|---|---|
| 16.30833 | NA | -0.0130481 | + | NA | + | 7 | -2714.545 | 5443.219 | 0.0000000 | 3.119794e-01 |
| 16.20379 | NA | -0.0145954 | + | + | + | 8 | -2713.539 | 5443.245 | 0.0252732 | 3.080619e-01 |
| 15.20113 | + | -0.0152163 | + | + | + | 11 | -2712.142 | 5446.592 | 3.3730104 | 5.776783e-02 |
| 15.69620 | NA | NA | NA | NA | NA | 2 | -2729.631 | 5463.275 | 20.0556995 | 1.377483e-05 |
| rowname | Estimate | Std. Error | t value | p |
|---|---|---|---|---|
| (Intercept) | 15.2011321 | 1.4946388 | 10.170439 | 0.0000000 |
| villageSarahandrano | -1.2766860 | 0.5058941 | -2.523623 | 0.0117946 |
| mass | -0.0152163 | 0.0053811 | -2.827708 | 0.0047973 |
| season3 | 1.4837053 | 0.4422159 | 3.355161 | 0.0008280 |
ASVs shannon -
| (Intercept) | grid | mass | season | sex | village | df | logLik | AICc | delta | weight |
|---|---|---|---|---|---|---|---|---|---|---|
| 1.312047 | + | NA | NA | + | + | 8 | -507.2202 | 1030.608 | 0.0000000 | 1.982241e-01 |
| 1.340837 | + | NA | + | + | + | 10 | -505.5083 | 1031.273 | 0.6651503 | 1.421415e-01 |
| 1.278259 | + | 0.0004579 | NA | + | + | 9 | -506.6027 | 1031.415 | 0.8070416 | 1.324066e-01 |
| 1.279583 | + | NA | NA | NA | + | 7 | -508.8161 | 1031.762 | 1.1545417 | 1.112886e-01 |
| 1.311781 | + | NA | + | NA | + | 9 | -506.9437 | 1032.097 | 1.4890425 | 9.414883e-02 |
| 1.313407 | + | 0.0003713 | + | + | + | 11 | -505.1215 | 1032.551 | 1.9432504 | 7.502136e-02 |
| 1.561372 | NA | NA | NA | NA | NA | 2 | -524.6267 | 1053.267 | 22.6594349 | 2.380798e-06 |
| rowname | Estimate | Std. Error | t value | p |
|---|---|---|---|---|
| (Intercept) | 1.3134074 | 0.1179093 | 11.139138 | 0.0000000 |
| villageMandena | 0.1532312 | 0.0380378 | 4.028390 | 0.0000611 |
| villageSarahandrano | 0.1013163 | 0.0399090 | 2.538680 | 0.0113026 |
| gridflooded_rice | 0.2593851 | 0.1183649 | 2.191403 | 0.0286905 |
ASVs Phylo -
| (Intercept) | grid | mass | season | sex | village | df | logLik | AICc | delta | weight |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.1885448 | NA | 0.0001073 | NA | NA | + | 5 | 975.7613 | -1941.453 | 0.0000000 | 0.1885797402 |
| 0.1972880 | NA | NA | NA | NA | + | 4 | 974.6685 | -1941.291 | 0.1623518 | 0.1738764609 |
| 0.1990704 | NA | NA | + | NA | + | 6 | 976.3535 | -1940.610 | 0.8435815 | 0.1236840330 |
| 0.1921366 | NA | 0.0000841 | + | NA | + | 7 | 976.9995 | -1939.869 | 1.5842374 | 0.0854047941 |
| 0.1889857 | NA | 0.0001121 | NA | + | + | 6 | 975.8151 | -1939.533 | 1.9204504 | 0.0721895817 |
| 0.1979476 | + | 0.0000674 | + | + | + | 11 | 978.8909 | -1935.474 | 5.9792803 | 0.0094866048 |
| 0.2116880 | NA | NA | NA | NA | NA | 2 | 965.3441 | -1926.674 | 14.7787493 | 0.0001165012 |
| rowname | Estimate | Std. Error | t value | p |
|---|---|---|---|---|
| (Intercept) | 0.1979476 | 0.0213743 | 9.260995 | 0.0000000 |
| villageSarahandrano | 0.0257403 | 0.0072346 | 3.557929 | 0.0003943 |
ASVs richness -
| (Intercept) | grid | mass | season | sex | df | logLik | AICc | delta | weight |
|---|---|---|---|---|---|---|---|---|---|
| 9.781250 | NA | NA | NA | + | 3 | -527.9185 | 1061.979 | 0.000000 | 0.365162710 |
| 8.426021 | NA | 0.1510793 | NA | + | 4 | -527.6396 | 1063.517 | 1.538295 | 0.169219317 |
| 10.867052 | NA | NA | NA | NA | 2 | -530.1754 | 1064.421 | 2.442486 | 0.107673103 |
| 7.352543 | + | 0.1810116 | + | + | 8 | -527.0658 | 1071.010 | 9.030628 | 0.003994942 |
| rowname | Estimate | Std. Error | t value | p |
|---|---|---|---|---|
| (Intercept) | 7.352543 | 2.478996 | 2.965936 | 0.0034629 |
ASVs shannon -
| (Intercept) | grid | mass | season | sex | df | logLik | AICc | delta | weight |
|---|---|---|---|---|---|---|---|---|---|
| 1.590274 | NA | NA | NA | NA | 2 | -74.61933 | 153.3092 | 0.000000 | 0.377978549 |
| 1.572888 | NA | NA | NA | + | 3 | -74.50860 | 155.1592 | 1.849974 | 0.149882310 |
| 1.639601 | + | -0.0024979 | + | + | 8 | -73.80826 | 164.4946 | 11.185328 | 0.001408005 |
| rowname | Estimate | Std. Error | t value | p |
|---|---|---|---|---|
| (Intercept) | 1.639601 | 0.1804806 | 9.08464 | 0 |
ASVs Phylo -
| (Intercept) | grid | mass | season | sex | df | logLik | AICc | delta | weight |
|---|---|---|---|---|---|---|---|---|---|
| 0.2070646 | NA | NA | NA | NA | 2 | 195.3066 | -386.5425 | 0.000000 | 0.385942981 |
| 0.2237143 | NA | -0.0016738 | NA | NA | 3 | 195.4751 | -384.8081 | 1.734387 | 0.162145803 |
| 0.2346814 | + | -0.0019201 | + | + | 8 | 196.0344 | -375.1907 | 11.351855 | 0.001322815 |
| rowname | Estimate | Std. Error | t value | p |
|---|---|---|---|---|
| (Intercept) | 0.2346814 | 0.0379337 | 6.186621 | 0 |
Fit a generalized linear mixed-effects model (GLMM) to check
differences in ASVs alpha diversity between land uses
Fixed variable: grid
Random variables: village, season
Tukey comparisons:
1 = semi-intact forest
2 = secondary_forest
3 = brushy_regrowth
4 = agriculture
5 = agroforest
6 = flooded_rice
7 = village
Community similarity between land uses in the population level.
Population = aggregation of all individuals from the same grid.
In order to mitigate the bias in hosts abundance across grids, I
randomly sample min number of individuals (min = no. of individuals in
the smallest grid) from all the grids.
Then, I aggregate all the individuals from the same grid and calculate
similarity between grids.
I repeat this 100 times and average the results.
Jaccard
| PERMANOVA | PERMDIST |
|---|---|
| 0.001 | 0.0024199 |
| agriculture | flooded_rice | |
|---|---|---|
| flooded_rice | 0.0015 | NA |
| village | 0.0015 | 0.002 |
| p adj | |
|---|---|
| flooded_rice-agriculture | 0.4454326 |
| village-agriculture | 0.0016623 |
| village-flooded_rice | 0.0469547 |
Connectivity of distance matrix with threshold dissimilarity 1 Data
are disconnected: 4 groups Groups sizes 1 2 3 4 245 1 1 1
Bray-Curtis
| PERMANOVA | PERMDIST |
|---|---|
| 0.001 | 0.0293255 |
| agriculture | flooded_rice | |
|---|---|---|
| flooded_rice | 0.0015 | NA |
| village | 0.0015 | 0.012 |
| p adj | |
|---|---|
| flooded_rice-agriculture | 0.9989345 |
| village-agriculture | 0.0272892 |
| village-flooded_rice | 0.0444988 |
Weighted UniFrac
| PERMANOVA | PERMDIST |
|---|---|
| 0.999 | 0.8058829 |
| agriculture | flooded_rice | |
|---|---|---|
| flooded_rice | 0.997 | NA |
| village | 0.997 | 0.997 |
| p adj | |
|---|---|
| flooded_rice-agriculture | 0.8787104 |
| village-agriculture | 0.9456352 |
| village-flooded_rice | 0.8117309 |
Jaccard
| PERMANOVA | PERMDIST |
|---|---|
| 0.001 | 0 |
| semi-intact_forest | agriculture | flooded_rice | |
|---|---|---|---|
| agriculture | 0.0080 | NA | NA |
| flooded_rice | 0.0140 | 0.008 | NA |
| village | 0.0096 | 0.006 | 0.009 |
| p adj | |
|---|---|
| agriculture-semi-intact_forest | 0.0000000 |
| flooded_rice-semi-intact_forest | 0.0000041 |
| village-semi-intact_forest | 0.0000006 |
| flooded_rice-agriculture | 0.2930703 |
| village-agriculture | 0.4296676 |
| village-flooded_rice | 0.9726514 |
Connectivity of distance matrix with threshold dissimilarity 1 Data
are connected
Bray-Curtis
| PERMANOVA | PERMDIST |
|---|---|
| 0.001 | 0 |
| semi-intact_forest | agriculture | flooded_rice | |
|---|---|---|---|
| agriculture | 0.0150 | NA | NA |
| flooded_rice | 0.0420 | 0.049 | NA |
| village | 0.0255 | 0.006 | 0.0255 |
| p adj | |
|---|---|
| agriculture-semi-intact_forest | 0.0000001 |
| flooded_rice-semi-intact_forest | 0.0000162 |
| village-semi-intact_forest | 0.0000334 |
| flooded_rice-agriculture | 0.3190588 |
| village-agriculture | 0.0198556 |
| village-flooded_rice | 0.9397812 |
Weighted UniFrac
| PERMANOVA | PERMDIST |
|---|---|
| 1 | 0.1193888 |
| semi-intact_forest | agriculture | flooded_rice | |
|---|---|---|---|
| agriculture | 1 | NA | NA |
| flooded_rice | 1 | 1 | NA |
| village | 1 | 1 | 1 |
| p adj | |
|---|---|
| agriculture-semi-intact_forest | 0.1492919 |
| flooded_rice-semi-intact_forest | 0.2061437 |
| village-semi-intact_forest | 0.3851565 |
| flooded_rice-agriculture | 0.9998780 |
| village-agriculture | 0.5612884 |
| village-flooded_rice | 0.8320159 |
Jaccard
| PERMANOVA | PERMDIST |
|---|---|
| 0.801 | 0.4726243 |
| semi-intact_forest | agriculture | |
|---|---|---|
| agriculture | 0.982 | NA |
| flooded_rice | 0.855 | 0.855 |
| p adj | |
|---|---|
| agriculture-semi-intact_forest | 0.7578514 |
| flooded_rice-semi-intact_forest | 0.9881757 |
| flooded_rice-agriculture | 0.5014285 |
Connectivity of distance matrix with threshold dissimilarity 1 Data
are connected
Bray-Curtis
| PERMANOVA | PERMDIST |
|---|---|
| 0.133 | 0.4744702 |
| semi-intact_forest | agriculture | |
|---|---|---|
| agriculture | 0.327 | NA |
| flooded_rice | 0.033 | 0.375 |
| p adj | |
|---|---|
| agriculture-semi-intact_forest | 0.9967033 |
| flooded_rice-semi-intact_forest | 0.7257462 |
| flooded_rice-agriculture | 0.4476865 |
Weighted UniFrac
| PERMANOVA | PERMDIST |
|---|---|
| 0.88 | 0.4652119 |
| semi-intact_forest | agriculture | |
|---|---|---|
| agriculture | 0.975 | NA |
| flooded_rice | 0.975 | 0.975 |
| p adj | |
|---|---|
| agriculture-semi-intact_forest | 0.9455421 |
| flooded_rice-semi-intact_forest | 0.5517904 |
| flooded_rice-agriculture | 0.5092334 |